Robust design of adaptive neural controllers for unknown nonlinear systems

Ziqian Liu, R. E. Torres, Miltiadis Kotinis
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Abstract

In this paper, we extend our previous research results from the stabilization of dynamic neural networks to the stabilization of unknown nonlinear systems, and present an approach of H∞ control for nonlinear systems via dynamic neural networks. The proposed H∞ controller is intended to attenuate the adverse impact of modeling error, considered as a disturbance, to a prescribed level with stability margins. A numerical example demonstrates the performance of stabilizing control on an unstable unknown nonlinear system.
未知非线性系统自适应神经控制器的鲁棒设计
本文将以往的研究成果从动态神经网络的镇定推广到未知非线性系统的镇定,提出了一种利用动态神经网络对非线性系统进行H∞控制的方法。所提出的H∞控制器旨在将建模误差的不利影响(视为干扰)衰减到具有稳定裕度的规定水平。数值算例验证了该方法对不稳定未知非线性系统的镇定控制性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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